Acquiring remote shared variable directory information in a parallel computer

ABSTRACT

Methods, parallel computers, and computer program products for acquiring remote shared variable directory (SVD) information in a parallel computer are provided. Embodiments include a runtime optimizer determining that a first thread of a first task requires shared resource data stored in a memory partition corresponding to a second thread of a second task. Embodiments also include the runtime optimizer requesting from the second thread, in response to determining that the first thread of the first task requires the shared resource data, SVD information associated with the shared resource data. Embodiments also include the runtime optimizer receiving from the second thread, the SVD information associated with the shared resource data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 13/718,327, filed on Dec. 18,2012.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, parallel computers, and computer program products for acquiringremote shared variable directory (SVD) information in a parallelcomputer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same application (split up and specially adapted) on multipleprocessors in order to obtain results faster. Parallel computing isbased on the fact that the process of solving a problem usually can bedivided into smaller jobs, which may be carried out simultaneously withsome coordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing jobs via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource, thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In atree network, the nodes typically are connected into a binary tree: eachnode has a parent and two children (although some nodes may only havezero children or one child, depending on the hardware configuration). Incomputers that use a torus and a tree network, the two networkstypically are implemented independently of one another, with separaterouting circuits, separate physical links, and separate message buffers.

A torus network lends itself to point to point operations, but a treenetwork typically is inefficient in point to point communication. A treenetwork, however, does provide high bandwidth and low latency forcertain collective operations, message passing operations where allcompute nodes participate simultaneously, such as, for example, anallgather.

There is at this time a general trend in computer processor developmentto move from multi-core to many-core processors: from dual-, tri-,quad-, hexa-, octo-core chips to ones with tens or even hundreds ofcores. In addition, multi-core chips mixed with simultaneousmultithreading, memory-on-chip, and special-purpose heterogeneous corespromise further performance and efficiency gains, especially inprocessing multimedia, recognition and networking applications. Thistrend is impacting the supercomputing world as well, where largetransistor count chips are more efficiently used by replicating cores,rather than building chips that are very fast but very inefficient interms of power utilization.

At the same time, the network link speed and number of links into andout of a compute node are dramatically increasing. IBM's BlueGene/Q™supercomputer, for example, has a five-dimensional torus network, whichimplements ten bidirectional data communications links per computenode—and BlueGene/Q supports many thousands of compute nodes. To keepthese links filled with data, DMA engines are employed, butincreasingly, the HPC community is interested in latency. In traditionalsupercomputers with pared-down operating systems, there is little or nomulti-tasking within compute nodes. When a data communications link isunavailable, a task typically blocks or ‘spins’ on a data transmission,in effect, idling a processor until a data transmission resource becomesavailable. In the trend for more powerful individual processors, suchblocking or spinning has a bad effect on latency.

SUMMARY OF THE INVENTION

Methods, parallel computers, and computer program products for acquiringremote shared variable directory (SVD) information in a parallelcomputer are provided. Embodiments include a runtime optimizerdetermining that a first thread of a first task requires shared resourcedata stored in a memory partition corresponding to a second thread of asecond task. Embodiments also include the runtime optimizer requestingfrom the second thread, in response to determining that the first threadof the first task requires the shared resource data, SVD informationassociated with the shared resource data. Embodiments also include theruntime optimizer receiving from the second thread, the SVD informationassociated with the shared resource data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth a block and network diagram of an example parallelcomputer that implements acquiring remote SVD information according toembodiments of the present invention.

FIG. 2 sets forth a block diagram of an example compute node for use inparallel computers that implement acquiring remote SVD informationaccording to embodiments of the present invention.

FIG. 3A illustrates an example Point To Point Adapter for use inparallel computers that implement acquiring remote SVD informationaccording to embodiments of the present invention.

FIG. 3B illustrates an example Collective Operations Adapter for use inparallel computers that implement acquiring remote SVD informationaccording to embodiments of the present invention.

FIG. 4 illustrates an example data communications network optimized forpoint to point operations for use in parallel computers that implementacquiring remote SVD information according to embodiments of the presentinvention.

FIG. 5 illustrates an example data communications network optimized forcollective operations by organizing compute nodes in a tree for use inparallel computers that implement acquiring remote SVD informationaccording to embodiments of the present invention.

FIG. 6 sets forth a block diagram of an example protocol stack for usein parallel computers that implement acquiring remote SVD informationaccording to embodiments of the present invention.

FIG. 7 sets forth a functional block diagram of example datacommunications resources for use in parallel computers that implementacquiring remote SVD information according to embodiments of the presentinvention.

FIG. 8 sets forth a functional block diagram of an example DMAcontroller—in an architecture where the DMA controller is the only DMAcontroller on a compute node—and an origin endpoint and its targetendpoint are both located on the same compute node.

FIG. 9 sets forth a functional block diagram of an example PAMI for usein parallel computers that implement acquiring remote SVD informationaccording to embodiments of the present invention.

FIG. 10 sets forth a flow chart illustrating an example method ofacquiring remote SVD information in a parallel computer according toembodiments of the present invention.

FIG. 11 sets forth a flow chart illustrating a further example method ofacquiring remote SVD information in a parallel computer according toembodiments of the present invention.

FIG. 12 sets forth a flow chart illustrating a further example method ofacquiring remote SVD information in a parallel computer according toembodiments of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Example methods, computers, and computer program products for acquiringremote shared variable directory (SVD) information in a parallelcomputer according to embodiments of the present invention are describedwith reference to the accompanying drawings, beginning with FIG. 1. FIG.1 sets forth a block and network diagram of an example parallel computer(100) that implements acquiring remote SVD information according toembodiments of the present invention. The parallel computer (100) in theexample of FIG. 1 is coupled to non-volatile memory for the computer inthe form of data storage device (118), an output device for the computerin the form of printer (120), and an input/output device for thecomputer in the form of computer terminal (122). The parallel computer(100) in the example of FIG. 1 includes a plurality of compute nodes(102).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a tree network (106) which isoptimized for collective operations, and a torus network (108) which isoptimized point to point operations. Tree network (106) is a datacommunications network that includes data communications links connectedto the compute nodes so as to organize the compute nodes as a tree. Eachdata communications network is implemented with data communicationslinks among the compute nodes (102). The data communications linksprovide data communications for parallel operations among the computenodes of the parallel computer.

In addition, the compute nodes (102) of parallel computer are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperations for moving data among compute nodes of an operational group.A ‘reduce’ operation is an example of a collective operation thatexecutes arithmetic or logical functions on data distributed among thecompute nodes of an operational group. An operational group may beimplemented as, for example, an MPI ‘communicator,’ or a partitionedglobal address space (PGAS) ‘communicator.’

In the example of FIG. 1, each compute node includes memory and acompiler. For illustrative purposes, an example memory (198) and anexample compiler (195) are shown. According to embodiments of thepresent invention, the memory (198) is configured according to a PGASprogramming model. The compiler (195) of FIG. 1 includes a PGAS runtimeoptimizer (199) to aid in the execution of PGAS programming code of thecompiler.

In PGAS programming models like Unified Parallel C (UPC), theprogramming model is different than traditional distributed programmingmodels. In a PGAS model, a thread may have both private memory as wellas shared memory across the address space. That is, the memory ispartitioned to provide thread local memory to a thread as well as sharedmemory across the threads.

In PGAS style languages and programming models, the address space isglobal across the threads of a job. Even though the address spaces usedto construct a particular job may span multiple OSI's and are protectedby hardware, UPC allows access to these address spaces implicitlythrough language constructs such as the keyword ‘shared.’ This keywordallows the user to construct a variable in a line of code that allowsaccess across a number of threads, for example the following UPC codemay be used to perform vector addition:

#include <upc_relaxed.h> #define N 100*THREADS shared int v1[N], v2[N],v1plusv2[N]; void main( ) { int i; for(i=MYTHREAD; i<N; i+=THREADS)v1plusv2[i]=v1[i]+v2[i]; }

In this example, variable are parallelized across one hundred threads.As explained above, no explicit calls are used to implement parallelism.Instead, the keyword ‘shared’ is used to indicate the variable isparallelized across threads. That is, no knowledge of the layout of thethreads to the hardware is required for generating UPC code.

With PGAS programming models, the user writes code in a similar fashionto serial code (like C) and hints to a compiler when certain variablesor code segments can be parallelized, including the creation of sharedobjects. For example, in the Unified Parallel C (UPC) PGAS programmingmodel, shared objects (i.e., data structures accessible from all UPCthreads) form the basis of the UPC language. Examples of shared objectsinclude but are not limited to: shared scalers (includingstructures/unions/enumerations), shared arrays (including multi-blockedarray), shared pointers (with either shared or private targets), andshared locks.

Central to the PGAS programming models is the concept of shared objectaffinity. A shared object is affine to a particular thread if it islocal to that thread's memory. For example, in UPC, shared arrays may bedistributed among a plurality of threads so different pieces of thearray may have affinity to different threads. A compiler may utilize aruntime optimizer to help map and control resources of the threads.

A PGAS runtime optimizer is generally a module of computer programinstructions configured to identify, create, and allocate resources fora particular job. For example, a PGAS runtime optimizer may beconfigured to spawn and collect UPC threads, implement access to shareddata, perform pointer arithmetic on pointers to shared objects andimplement all the UPC intrinsic function calls (such as upc_phaseof,upc_barrier and upc_memget). A PGAS runtime optimizer may also begenerally configured to map the resources in an optimal way to availablehardware and begin execution of core code on the resources.

To help organize and control access to these shared resources, a PGASruntime optimizer may implement a Shared Variable Directory (SVD) thatis used to store locations of variables that are shared across thetasks. A PGAS runtime optimizer may use an SVD to look up and findresources within a UPC job. This may include looking up memory, thread,and other resource locations.

An SVD may be a table contained on each task of a node and is used tolook up remote resources of other tasks. In a particular embodiment, anSVD may include a partition for each thread where each partition of theSVD holds a list of those variable affine to a particular thread. TheSVD may also include another partition that is reserved for sharedvariables allocated statically or through collective operations. Sharedobjects may be referred to by an SVD handle, which is an opaque objectthat is internally indexed in the SVD. An SVD handle may contain thepartition number in the directory, and the index of the object in thepartition.

Multiple replicas of an SVD may exist in a system and the SVD oftenchanges at runtime because of UPC routines for dynamic data allocation.Because in the PGAS programming models, each thread may allocate andde-allocate shared variables independently of each other, changes tocopies of the SVD may require threads to communicate updates to eachother by acquiring remote SVD information from other tasks.

In the example of FIG. 1, the runtime optimizer (199) may includecomputer program instructions for acquiring remote SVD informationaccording to embodiments of the present invention. Specifically, theruntime optimizer (199) may include computer program instructions thatwhen executed by a computer processor cause the computer processor tofunction by partitioning memory (198) such that each thread is provideda partition of shared memory (197) and a partition of private memory(196). As explained above, the runtime optimizer (199) may also beconfigured to map resources across the partitions and to create an SVDto index these mappings. The runtime optimizer (199) may also beconfigured to determine that a first thread of a first task requiresshared resource data stored in a shared memory partition correspondingto a second thread of a second task. The runtime optimizer (199) mayalso be configured to request from the second thread, in response todetermining that the first thread of the first task requires the sharedresource data, the shared resource data and SVD information associatedwith the shared resource data. The runtime optimizer (199) may includecomputer program instructions that when executed by a computer processorcause the computer processor to function by receiving from the secondthread, the shared resource data and the SVD information.

To transfer information and data between the tasks, the PGAS runtimeoptimizer (199) may access a lower level message passing layer, such asa Parallel Active Message Interface (PAMI) (218) that implementsprimitives across the tasks in the job, including collective operations.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. In a broadcastoperation, all processes specify the same root process, whose buffercontents will be sent. Processes other than the root specify receivebuffers. After the operation, all buffers contain the message from theroot process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. All processes specify the same receive count. Thesend arguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer will be divided equally and dispersed to all processes (includingitself). Each compute node is assigned a sequential identifier termed a‘rank.’ After the operation, the root has sent sendcount data elementsto each process in increasing rank order. Rank 0 receives the firstsendcount data elements from the send buffer. Rank 1 receives the secondsendcount data elements from the send buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from computer node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations.

In addition to compute nodes, the example parallel computer (100)includes input/output (‘I/O’) nodes (110, 114) coupled to compute nodes(102) through one of the data communications networks (174). The I/Onodes (110, 114) provide I/O services between compute nodes (102) andI/O devices (118, 120, 122). I/O nodes (110, 114) are connected for datacommunications to I/O devices (118, 120, 122) through local area network(‘LAN’) (130). Computer (100) also includes a service node (116) coupledto the compute nodes through one of the networks (104). Service node(116) provides service common to pluralities of compute nodes, loadingprograms into the compute nodes, starting program execution on thecompute nodes, retrieving results of program operations on the computernodes, and so on. Service node (116) runs a service application (124)and communicates with users (128) through a service applicationinterface (126) that runs on computer terminal (122).

As the term is used here, a parallel active messaging interface or‘PAMI’ (218) is a system-level messaging layer in a protocol stack of aparallel computer that is composed of data communications endpoints eachof which is specified with data communications parameters for a threadof execution on a compute node of the parallel computer. The PAMI is a‘parallel’ interface in that many instances of the PAMI operate inparallel on the compute nodes of a parallel computer. The PAMI is an‘active messaging interface’ in that data communications messages in thePAMI are active messages, ‘active’ in the sense that such messagesimplement callback functions to advise of message dispatch andinstruction completion and so on, thereby reducing the quantity ofacknowledgment traffic, and the like, burdening the data communicationresources of the PAMI.

Each data communications endpoint of a PAMI is implemented as acombination of a client, a context, and a task. A ‘client’ as the termis used in PAMI operations is a collection of data communicationsresources dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. A ‘context’ as the term is used in PAMIoperations is composed of a subset of a client's collection of dataprocessing resources, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution. In atleast some embodiments, the context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context. A‘task’ as the term is used in PAMI operations refers to a canonicalentity, an integer or objection oriented programming object, thatrepresents in a PAMI a process of execution of the parallel application.That is, a task is typically implemented as an identifier of aparticular instance of an application executing on a compute node, acompute core on a compute node, or a thread of execution on amulti-threading compute core on a compute node.

In the example of FIG. 1, the compute nodes (102), as well as PAMIendpoints on the compute nodes, are coupled for data communicationsthrough the PAMI (218) and through data communications resources such ascollective network (106) and point-to-point network (108). In anyparticular communication of data, an origin endpoint and a targetendpoint can be any two endpoints on any of the compute nodes (102), ondifferent compute nodes, or two endpoints on the same compute node.Collective operations can have one origin endpoint and many targetendpoints, as in a BROADCAST, for example, or many origin endpoints andone target endpoint, as in a GATHER, for example. A sequence of datacommunications instructions, including instructions for collectiveoperations, resides in a work queue of a context and results in datatransfers among endpoints, origin endpoints and target endpoints. Datacommunications instructions, including instructions for collectiveoperations, are ‘active’ in the sense that the instructions implementcallback functions to advise of and implement instruction dispatch andinstruction completion, thereby reducing the quantity of acknowledgmenttraffic required on the network. Each such data communicationsinstruction or instruction for a collective operation effects a datatransfer or transfers, from one or more origin endpoints to one or moretarget endpoints, through some form of data communications resources,networks, shared memory segments, network adapters, DMA controllers, andthe like.

The arrangement of compute nodes, networks, and I/O devices making upthe example parallel computer illustrated in FIG. 1 are for explanationonly, not for limitation of the present invention. Parallel computerscapable of data communications in a PAMI according to embodiments of thepresent invention may include additional nodes, networks, devices, andarchitectures, not shown in FIG. 1, as will occur to those of skill inthe art. The parallel computer (100) in the example of FIG. 1 includessixteen compute nodes (102); some parallel computers that implementacquiring remote shared variable directory (SVD) information accordingto some embodiments of the present invention include thousands ofcompute nodes. In addition to Ethernet and JTAG, networks in such dataprocessing systems may support many data communications protocolsincluding for example TCP (Transmission Control Protocol), IP (InternetProtocol), and others as will occur to those of skill in the art.Various embodiments of the present invention may be implemented on avariety of hardware platforms in addition to those illustrated in FIG.1.

Acquiring remote shared variable directory (SVD) information accordingto embodiments of the present invention is generally implemented on aparallel computer that includes a plurality of compute nodes. In fact,such computers may include thousands of such compute nodes, with acompute node typically executing at least one instance of a parallelapplication. Each compute node is in turn itself a computer composed ofone or more computer processors, its own computer memory, and its owninput/output (‘I/O’) adapters. For further explanation, therefore, FIG.2 sets forth a block diagram of an example compute node (152) for use ina parallel computer that implement acquiring remote shared variabledirectory (SVD) information according to embodiments of the presentinvention. The compute node (152) of FIG. 2 includes one or morecomputer processors (164) as well as random access memory (‘RAM’) (156).Each processor (164) can support multiple hardware compute cores (165),and each such core can in turn support multiple threads of execution,hardware threads of execution as well as software threads. Eachprocessor (164) is connected to RAM (156) through a high-speed frontside bus (161), bus adapter (194), and a high-speed memory bus (154)—andthrough bus adapter (194) and an extension bus (168) to other componentsof the compute node. Stored in RAM (156) is an application program(158), a module of computer program instructions that carries outparallel, user-level data processing using parallel algorithms.

Also stored RAM (156) is an runtime optimizer (216), a library ofcomputer program instructions that carry out application-level parallelcommunications among compute nodes, including point to point operationsas well as collective operations. Although the application program cancall PAMI routines directly, the application program (158) oftenexecutes point-to-point data communications operations by callingsoftware routines in the application messaging module (215), which inturn is improved according to embodiments of the present invention touse PAMI functions to implement such communications. An applicationmessaging module can be developed from scratch to use a PAMI accordingto embodiments of the present invention, using a traditional programminglanguage such as the C programming language or C++, for example, andusing traditional programming methods to write parallel communicationsroutines that send and receive data among PAMI endpoints and computenodes through data communications networks or shared-memory transfers.

Also represented in RAM in the example of FIG. 2 is a PAMI (218).Readers will recognize, however, that the representation of the PAMI inRAM is a convention for ease of explanation rather than a limitation ofthe present invention, because the PAMI and its components, endpoints,clients, contexts, and so on, have particular associations with andinclusions of hardware data communications resources. In fact, the PAMIcan be implemented partly as software or firmware and hardware—or even,at least in some embodiments, entirely in hardware.

Also represented in RAM (156) in the example of FIG. 2 is a segment(227) of memory. According to embodiments of the present invention, theruntime optimizer (216) may be configured to partition the memory (227)such that each thread is provided a partition of shared memory (297) anda partition of private memory (296). As explained above, the runtimeoptimizer (216) may also be configured to map resources across thepartitions and to create an SVD to index these mappings. The runtimeoptimizer (216) may also be configured to determine that a first threadof a first task requires shared resource data stored in a shared memorypartition corresponding to a second thread of a second task. The runtimeoptimizer (216) may also be configured to request from the secondthread, in response to determining that the first thread of the firsttask requires the shared resource data, the shared resource data and SVDinformation associated with the shared resource data. The runtimeoptimizer (216) may include computer program instructions that whenexecuted by a computer processor cause the computer processor tofunction by receiving from the second thread, the shared resource dataand the SVD information.

In the example of FIG. 2, each processor or compute core has uniformaccess to the RAM (156) on the compute node, so that accessing a segmentof shared memory is equally fast regardless where the shared segment islocated in physical memory. In some embodiments, however, modules ofphysical memory are dedicated to particular processors, so that aprocessor may access local memory quickly and remote memory more slowly,a configuration referred to as a Non-Uniform Memory Access or ‘NUMA.’ Insuch embodiments, a segment of shared memory can be configured locallyfor one endpoint and remotely for another endpoint—or remotely from bothendpoints of a communication. From the perspective of an origin endpointtransmitting data through a segment of shared memory that is configuredremotely with respect to the origin endpoint, transmitting data throughthe segment of shared memory will appear slower that if the segment ofshared memory were configured locally with respect to the originendpoint—or if the segment were local to both the origin endpoint andthe target endpoint. This is the effect of the architecture representedby the compute node (152) in the example of FIG. 2 with all processorsand all compute cores coupled through the same bus to the RAM—that allaccesses to segments of memory shared among processes or processors onthe compute node are local—and therefore very fast.

Also stored in RAM (156) in the example compute node of FIG. 2 is anoperating system (162), a module of computer program instructions androutines for an application program's access to other resources of thecompute node. It is possible, in some embodiments at least, for anapplication program, an application messaging module, and a PAMI in acompute node of a parallel computer to run threads of execution with nouser login and no security issues because each such thread is entitledto complete access to all resources of the node. The quantity andcomplexity of duties to be performed by an operating system on a computenode in a parallel computer therefore can be somewhat smaller and lesscomplex than those of an operating system on a serial computer with manythreads running simultaneously with various level of authorization foraccess to resources. In addition, there is no video I/O on the computenode (152) of FIG. 2, another factor that decreases the demands on theoperating system. The operating system may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down or ‘lightweight’ version as it were, or anoperating system developed specifically for operations on a particularparallel computer. Operating systems that may be improved or simplifiedfor use in a compute node according to embodiments of the presentinvention include UNIX™, Linux™, Microsoft XP™, AIX™, IBM's i5/OS™, andothers as will occur to those of skill in the art.

The example compute node (152) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters for use in computers that implement acquiringremote shared variable directory (SVD) information according toembodiments of the present invention include modems for wiredcommunications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also used as amechanism for debugging embedded systems, providing a convenient “backdoor” into the system. The example compute node of FIG. 2 may be allthree of these: It typically includes one or more integrated circuitsinstalled on a printed circuit board and may be implemented as anembedded system having its own processor, its own memory, and its ownI/O capability. JTAG boundary scans through JTAG Slave (176) mayefficiently configure processor registers and memory in compute node(152) for use in data communications in a PAMI according to embodimentsof the present invention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a data communications network (108) that isoptimal for point to point message passing operations such as, forexample, a network configured as a three-dimensional torus or mesh.Point To Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186). For ease of explanation, the Point To Point Adapter (180)of FIG. 2 as illustrated is configured for data communications in threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in data communicationsin a PAMI of a parallel computer according to embodiments of the presentinvention may in fact be implemented so as to support communications intwo dimensions, four dimensions, five dimensions, and so on.

The data communications adapters in the example of FIG. 2 includes aCollective Operations Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations such as, for example, a networkconfigured as a binary tree. Collective Operations Adapter (188)provides data communications through three bidirectional links: two tochildren nodes (190) and one to a parent node (192).

The example compute node (152) includes a number of arithmetic logicunits (‘ALUs’). ALUs (166) are components of processors (164), and aseparate ALU (170) is dedicated to the exclusive use of collectiveoperations adapter (188) for use in performing the arithmetic andlogical functions of reduction operations. Computer program instructionsof a reduction routine in an application messaging module (215) or aPAMI (218) may latch an instruction for an arithmetic or logicalfunction into instruction register (169). When the arithmetic or logicalfunction of a reduction operation is a ‘sum’ or a ‘logical OR,’ forexample, collective operations adapter (188) may execute the arithmeticor logical operation by use of an ALU (166) in a processor (164) or,typically much faster, by use of the dedicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(‘DMA’) controller (225), a module of automated computing machinery thatimplements, through communications with other DMA engines on othercompute nodes, or on a same compute node, direct memory access to andfrom memory on its own compute node as well as memory on other computenodes. Direct memory access is a way of reading and writing to and frommemory of compute nodes with reduced operational burden on computerprocessors (164); a CPU initiates a DMA transfer, but the CPU does notexecute the DMA transfer. A DMA transfer essentially copies a block ofmemory from one compute node to another, or between RAM segments ofapplications on the same compute node, from an origin to a target for aPUT operation, from a target to an origin for a GET operation.

For further explanation, FIG. 3A illustrates an example of a Point ToPoint Adapter (180) useful in parallel computers that implementacquiring remote shared variable directory (SVD) information accordingto embodiments of the present invention. Point To Point Adapter (180) isdesigned for use in a data communications network optimized for point topoint operations, a network that organizes compute nodes in athree-dimensional torus or mesh. Point To Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). Point To Point Adapter (180) also provides datacommunication along a y-axis through four unidirectional datacommunications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). Point ToPoint Adapter (180) in also provides data communication along a z-axisthrough four unidirectional data communications links, to and from thenext node in the −z direction (186) and to and from the next node in the+z direction (185). For ease of explanation, the Point To Point Adapter(180) of FIG. 3A as illustrated is configured for data communications inonly three dimensions, x, y, and z, but readers will recognize thatPoint To Point Adapters optimized for point-to-point operations in aparallel computer that implements acquiring remote shared variabledirectory (SVD) information according to embodiments of the presentinvention may in fact be implemented so as to support communications intwo dimensions, four dimensions, five dimensions, and so on. Severalsupercomputers now use five dimensional mesh or torus networks,including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 3B illustrates an example of a CollectiveOperations Adapter (188) useful in a parallel computer that implementsacquiring remote shared variable directory (SVD) information accordingto embodiments of the present invention. Collective Operations Adapter(188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. Collective Operations Adapter (188) in theexample of FIG. 3B provides data communication to and from two childrennodes through four unidirectional data communications links (190).Collective Operations Adapter (188) also provides data communication toand from a parent node through two unidirectional data communicationslinks (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in parallel computers that implementacquiring remote shared variable directory (SVD) information accordingto embodiments of the present invention. In the example of FIG. 4, dotsrepresent compute nodes (102) of a parallel computer, and the dottedlines between the dots represent data communications links (103) betweencompute nodes. The data communications links are implemented withpoint-to-point data communications adapters similar to the oneillustrated for example in FIG. 3A, with data communications links onthree axis, x, y, and z, and to and fro in six directions +x (181), −x(182), +y (183), −y (184), +z (185), and −z (186). The links and computenodes are organized by this data communications network optimized forpoint-to-point operations into a three dimensional mesh (105). The mesh(105) has wrap-around links on each axis that connect the outermostcompute nodes in the mesh (105) on opposite sides of the mesh (105).These wrap-around links form a torus (107). Each compute node in thetorus has a location in the torus that is uniquely specified by a set ofx, y, z coordinates. Readers will note that the wrap-around links in they and z directions have been omitted for clarity, but are configured ina similar manner to the wrap-around link illustrated in the x direction.For clarity of explanation, the data communications network of FIG. 4 isillustrated with only 27 compute nodes, but readers will recognize thata data communications network optimized for point-to-point operations ina parallel computer that implements acquiring remote shared variabledirectory (SVD) information according to embodiments of the presentinvention may contain only a few compute nodes or may contain thousandsof compute nodes. For ease of explanation, the data communicationsnetwork of FIG. 4 is illustrated with only three dimensions: x, y, andz, but readers will recognize that a data communications networkoptimized for point-to-point operations may in fact be implemented intwo dimensions, four dimensions, five dimensions, and so on. Asmentioned, several supercomputers now use five dimensional mesh or torusnetworks, including IBM's Blue Gene Q™.

For further explanation, FIG. 5 illustrates an example datacommunications network (106) optimized for collective operations byorganizing compute nodes in a tree. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with collective operations data communicationsadapters similar to the one illustrated for example in FIG. 3B, witheach node typically providing data communications to and from twochildren nodes and data communications to and from a parent node, withsome exceptions. Nodes in a binary tree may be characterized as a rootnode (202), branch nodes (204), and leaf nodes (206). The root node(202) has two children but no parent. The leaf nodes (206) each has aparent, but leaf nodes have no children. The branch nodes (204) each hasboth a parent and two children. The links and compute nodes are therebyorganized by this data communications network optimized for collectiveoperations into a binary tree (106). For clarity of explanation, thedata communications network of FIG. 5 is illustrated with only 31compute nodes, but readers will recognize that a data communicationsnetwork optimized for collective operations for use in parallelcomputers that implement acquiring remote shared variable directory(SVD) information according to embodiments of the present invention maycontain only a few compute nodes or hundreds or thousands of computenodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifiesan instance of a parallel application that is executing on a computenode. That is, the rank is an application-level identifier. Using therank to identify a node assumes that only one such instance of anapplication is executing on each node. A compute node can, however,support multiple processors, each of which can support multipleprocessing cores—so that more than one process or instance of anapplication can easily be present under execution on any given computenode—or in all the compute nodes, for that matter. To the extent thatmore than one instance of an application executes on a single computenode, the rank identifies the instance of the application as such ratherthan the compute node. A rank uniquely identifies an application'slocation in the tree network for use in both point-to-point andcollective operations in the tree network. The ranks in this example areassigned as integers beginning with ‘0’ assigned to the root instance orroot node (202), ‘1’ assigned to the first node in the second layer ofthe tree, ‘2’ assigned to the second node in the second layer of thetree, ‘3’ assigned to the first node in the third layer of the tree, ‘4’assigned to the second node in the third layer of the tree, and so on.For ease of illustration, only the ranks of the first three layers ofthe tree are shown here, but all compute nodes, or rather allapplication instances, in the tree network are assigned a unique rank.Such rank values can also be assigned as identifiers of applicationinstances as organized in a mesh or torus network.

For further explanation, FIG. 6 sets forth a block diagram of an exampleprotocol stack useful in parallel computers that implement acquiringremote shared variable directory (SVD) information according toembodiments of the present invention. The example protocol stack of FIG.6 includes a hardware layer (214), a system messaging layer (212), anapplication messaging layer (210), and an application layer (208). Forease of explanation, the protocol layers in the example stack of FIG. 6are shown connecting an origin compute node (222) and a target computenode (224), although it is worthwhile to point out that in embodimentsthat effect DMA data transfers, the origin compute node and the targetcompute node can be the same compute node. The granularity of connectionthrough the system messaging layer (212), which is implemented with aPAMI (218), is finer than merely compute node to compute node—because,again, communications among endpoints often is communications amongendpoints on the same compute node. For further explanation, recall thatthe PAMI (218) connects endpoints, connections specified by combinationsof clients, contexts, and tasks, each such combination being specific toa thread of execution on a compute node, with each compute node capableof supporting many threads and therefore many endpoints. Every endpointtypically can function as both an origin endpoint or a target endpointfor data transfers through a PAMI, and both the origin endpoint and itstarget endpoint can be located on the same compute node. So an origincompute node (222) and its target compute node (224) can in fact, andoften will, be the same compute node.

The application layer (208) provides communications among instances of aparallel application (158) running on the compute nodes (222, 224) byinvoking functions in an application messaging module (215) installed oneach compute node. Communications among instances of the applicationthrough messages passed between the instances of the application.Applications may communicate messages invoking function of anapplication programming interface (‘API’) exposed by the applicationmessaging module (215). In this approach, the application messagingmodule (215) exposes a traditional interface, such as an API of an MPIlibrary, to the application program (158) so that the applicationprogram can gain the benefits of a PAMI, reduced network traffic,callback functions, and so on, with no need to recode the application.Alternatively, if the parallel application is programmed to use PAMIfunctions, the application can call the PAMI functions directly, withoutgoing through the application messaging module.

The example protocol stack of FIG. 6 includes a system messaging layer(212) implemented here as a PAMI (218). The PAMI provides system-leveldata communications functions that support messaging in the applicationlayer (602) and the application messaging layer (610). Such system-levelfunctions are typically invoked through an API exposed to theapplication messaging modules (215) in the application messaging layer(210). Although developers can in fact access a PAMI API directly bycoding an application to do so, a PAMI's system-level functions in thesystem messaging layer (212) in many embodiments are isolated from theapplication layer (208) by the application messaging layer (210), makingthe application layer somewhat independent of system specific details.With an application messaging module presenting a standard MPI API to anapplication, for example, with the application messaging module retooledto use the PAMI to carry out the low-level messaging functions, theapplication gains the benefits of a PAMI with no need to incur theexpense of reprogramming the application to call the PAMI directly.Because, however, some applications will in fact be reprogrammed to callthe PAMI directly, all entities in the protocol stack above the PAMI areviewed by PAMI as applications. When PAMI functions are invoked byentities above the PAMI in the stack, the PAMI makes no distinctionwhether the caller is in the application layer or the applicationmessaging layer, no distinction whether the caller is an application assuch or an MPI library function invoked by an application. As far as thePAMI is concerned, any caller of a PAMI function is an application.

The protocol stack of FIG. 6 includes a hardware layer (634) thatdefines the physical implementation and the electrical implementation ofaspects of the hardware on the compute nodes such as the bus, networkcabling, connector types, physical data rates, data transmissionencoding and many other factors for communications between the computenodes (222) on the physical network medium. In parallel computers thatimplement acquiring remote shared variable directory (SVD) informationwith DMA controllers according to embodiments of the present invention,the hardware layer includes DMA controllers and network links, includingrouters, packet switches, and the like.

For further explanation of data communications resources assigned incollections to PAMI clients, FIG. 7 sets forth a block diagram ofexample data communications resources (220) useful in parallel computersthat implement acquiring remote shared variable directory (SVD)information according to embodiments of the present invention. The datacommunications resources of FIG. 7 include a gigabit Ethernet adapter(238), an Infiniband adapter (240), a Fibre Channel adapter (242), a PCIExpress adapter (246), a collective operations network configured as atree (106), shared memory (227), DMA controllers (225, 226), and anetwork (108) configured as a point-to-point torus or mesh like thenetwork described above with reference to FIG. 4. A PAMI is configuredwith clients, each of which is in turn configured with certaincollections of such data communications resources—so that, for example,the PAMI client (302) in the PAMI (218) in the example of FIG. 7 canhave dedicated to its use a collection of data communications resourcescomposed of six segments (227) of shared memory, six Gigabit Ethernetadapters (238), and six Infiniband adapters (240). And the PAMI client(304) can have dedicated to its use six Fibre Channel adapters (242), aDMA controller (225), a torus network (108), and five segments (227) ofshared memory. And so on.

The DMA controllers (225, 226) in the example of FIG. 7 each isconfigured with DMA control logic in the form of a DMA engine (228,229), an injection FIFO buffer (230), and a receive FIFO buffer (232).The DMA engines (228, 229) can be implemented as hardware components,logic networks of a DMA controller, in firmware, as software operatingan embedded controller, as various combinations of software, firmware,or hardware, and so on. Each DMA engine (228, 229) operates on behalf ofendpoints to send and receive DMA transfer data through the network(108). The DMA engines (228, 229) operate the injection buffers (230,232) by processing first-in-first-out descriptors (234, 236) in thebuffers, hence the designation ‘injection FIFO’ and ‘receive FIFO.’

For further explanation, here is an example use case, a description ofthe overall operation of an example PUT DMA transfer using the DMAcontrollers (225, 226) and network (108) in the example of FIG. 7: Anoriginating application (158), which is typically one instance of aparallel application running on a compute node, places a quantity oftransfer data (494) at a location in its RAM (155). The application(158) then calls a post function (480) on a context (512) of an originendpoint (352), posting a PUT instruction (390) into a work queue (282)of the context (512); the PUT instruction (390) specifies a targetendpoint (354) to which the transfer data is to be sent as well assource and destination memory locations. The application then calls anadvance function (482) on the context (512). The advance function (482)finds the new PUT instruction in its work queue (282) and inserts a datadescriptor (234) into the injection FIFO of the origin DMA controller(225); the data descriptor includes the source and destination memorylocations and the specification of the target endpoint. The origin DMAengine (225) then transfers the data descriptor (234) as well as thetransfer data (494) through the network (108) to the DMA controller(226) on the target side of the transaction. The target DMA engine(229), upon receiving the data descriptor and the transfer data, placesthe transfer data (494) into the RAM (156) of the target application atthe location specified in the data descriptor and inserts into thetarget DMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) or application instancecalls an advance function (483) on a context (513) of the targetendpoint (354). The advance function (483) checks the communicationsresources assigned to its context (513) for incoming messages, includingchecking the receive FIFO (232) of the target DMA controller (226) fordata descriptors that specify the target endpoint (354). The advancefunction (483) finds the data descriptor for the PUT transfer andadvises the target application (159) that its transfer data has arrived.A GET-type DMA transfer works in a similar manner, with somedifferences, including, of course, the fact that transfer data flows inthe opposite direction. Similarly, typical SEND transfers also operatesimilarly, some with rendezvous protocols, some with eager protocols,with data transmitted in packets over the a network through non-DMAnetwork adapters or through DMA controllers.

The example of FIG. 7 includes two DMA controllers (225, 226). DMAtransfers between endpoints on separate compute nodes use two DMAcontrollers, one on each compute node. Compute nodes can be implementedwith multiple DMA controllers so that many or even all DMA transferseven among endpoints on a same compute node can be carried out using twoDMA engines. In some embodiments at least, however, a compute node, likethe example compute node (152) of FIG. 2, has only one DMA engine, sothat that DMA engine can be use to conduct both sides of transfersbetween endpoints on that compute node. For further explanation of thisfact, FIG. 8 sets forth a functional block diagram of an example DMAcontroller (225) operatively coupled to a network (108)—in anarchitecture where this DMA controller (225) is the only DMA controlleron a compute node—and an origin endpoint (352) and its target endpoint(354) are both located on the same compute node (152). In the example ofFIG. 8, a single DMA engine (228) operates with two threads of execution(502, 504) on behalf of endpoints (352, 354) on a same compute node tosend and receive DMA transfer data through a segment (227) of sharedmemory. A transmit thread (502) injects transfer data into the network(108) as specified in data descriptors (234) in an injection FIFO buffer(230), and a receive thread (502) receives transfer data from thenetwork (108) as specified in data descriptors (236) in a receive FIFObuffer (232).

The overall operation of an example PUT DMA transfer with the DMAcontrollers (225) and the network (108) in the example of FIG. 8 is: Anoriginating application (158), that is actually one of multipleinstances (158, 159) of a parallel application running on a compute node(152) in separate threads of execution, places a quantity of transferdata (494) at a location in its RAM (155). The application (158) thencalls a post function (480) on a context (512) of an origin endpoint(352), posting a PUT instruction (390) into a work queue (282) of thecontext (512); the PUT instruction specifies a target endpoint (354) towhich the transfer data is to be sent as well as source and destinationmemory locations. The application (158) then calls an advance function(482) on the context (512). The advance function (482) finds the new PUTinstruction (390) in its work queue (282) and inserts a data descriptor(234) into the injection FIFO of the DMA controller (225); the datadescriptor includes the source and destination memory locations and thespecification of the target endpoint. The DMA engine (225) thentransfers by its transmit and receive threads (502, 504) through thenetwork (108) the data descriptor (234) as well as the transfer data(494). The DMA engine (228), upon receiving by its receive thread (504)the data descriptor and the transfer data, places the transfer data(494) into the RAM (156) of the target application and inserts into theDMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) calls an advancefunction (483) on a context (513) of the target endpoint (354). Theadvance function (483) checks the communications resources assigned toits context for incoming messages, including checking the receive FIFO(232) of the DMA controller (225) for data descriptors that specify thetarget endpoint (354). The advance function (483) finds the datadescriptor for the PUT transfer and advises the target application (159)that its transfer data has arrived. Again, a GET-type DMA transfer worksin a similar manner, with some differences, including, of course, thefact that transfer data flows in the opposite direction. And typicalSEND transfers also operate similarly, some with rendezvous protocols,some with eager protocols, with data transmitted in packets over the anetwork through non-DMA network adapters or through DMA controllers.

By use of an architecture like that illustrated and described withreference to FIG. 8, a parallel application or an application messagingmodule that is already programmed to use DMA transfers can gain thebenefit of the speed of DMA data transfers among endpoints on the samecompute node with no need to reprogram the applications or theapplication messaging modules to use the network in other modes. In thisway, an application or an application messaging module, alreadyprogrammed for DMA, can use the same DMA calls through a same API forDMA regardless whether subject endpoints are on the same compute node oron separate compute nodes.

FIG. 9 sets forth a functional block diagram of an example PAMI (218)useful in parallel computers that implement acquiring remote sharedvariable directory (SVD) information according to embodiments of thepresent invention in which the example PAMI operates, on behalf of anapplication (158), with multiple application messaging modules (502-510)simultaneously. The application (158) can have multiple messages intransit simultaneously through each of the application messaging modules(502-510). Each context (512-520) carries out, through post and advancefunctions, data communications for the application on datacommunications resources in the exclusive possession, in each client, ofthat context. Each context carries out data communications operationsindependently and in parallel with other contexts in the same or otherclients. In the example FIG. 9, each client (532-540) includes acollection of data communications resources (522-530) dedicated to theexclusive use of an application-level data processing entity, one of theapplication messaging modules (502-510):

-   -   IBM MPI Library (502) operates through context (512) data        communications resources (522) dedicated to the use of PAMI        client (532),    -   MPICH Library (504) operates through context (514) data        communications resources (524) dedicated to the use of PAMI        client (534),    -   Unified Parallel C (‘UPC’) Library (506) operates through        context (516) data communications resources (526) dedicated to        the use of PAMI client (536),    -   Partitioned Global Access Space (‘PGAS’) Runtime Library (508)        operates through context (518) data communications resources        (528) dedicated to the use of PAMI client (538), and    -   Aggregate Remote Memory Copy Interface (‘ARMCI’) Library (510)        operates through context (520) data communications resources        (530) dedicated to the use of PAMI client (540).

Context functions, explained here with regard to references (472-482) onFIG. 9, include functions to create (472) and destroy (474) contexts,functions to lock (476) and unlock (478) access to a context, andfunctions to post (480) and advance (480) work in a context. For ease ofexplanation, the context functions (472-482) are illustrated in only oneexpanded context (512); readers will understand, however, that all PAMIcontexts have similar context functions. The create (472) and destroy(474) functions are, in an object-oriented sense, constructors anddestructors. In the example embodiments described in thisspecifications, post (480) and advance (482) functions on a context arecritical sections, not thread safe. Applications using suchnon-reentrant functions must somehow ensure that critical sections areprotected from re-entrant use.

Posts and advances (480, 482 on FIG. 9) are functions called on acontext, either in a C-type function with a context ID as a parameter,or in object oriented practice where the calling entity possesses areference to a context or a context object as such and the posts andadvances are member methods of a context object.

FIG. 10 sets forth a flow chart illustrating an example method ofacquiring remote shared variable directory (SVD) information of aparallel computer according to embodiments of the present invention. AnSVD may be a distributed symbol table that indexes shared objects byhandles or keys. In the SVD, each handle or key has a correspondinglocal address within a memory partition of a thread. Threads may beorganized into tasks. Each thread may have a partition of shared memoryand private memory. In the example of FIG. 10, a first task (1040)includes a first thread (1030) and a plurality of other threads (1098)and a second task (1041) includes a second thread (1031) and a pluralityof other threads (1099). Memory (1045) is divided into a shared memorypartition (105) and a private memory partition (1051) for the threads ofthe first task (1040).

The method of FIG. 10 includes a runtime optimizer (1000) determining(1002) that a first thread (1030) of a first task (1040) requires sharedresource data (1080) stored in a memory partition (1060) correspondingto a second thread (1031) of a second task (1041). Determining (1002)that a first thread (1030) of a first task (1040) requires sharedresource data (1080) may be carried out by identifying a segment of codefor execution on the first thread that utilizes the shared resource dataof the second thread.

The method of FIG. 10 includes requesting (1004) from the second thread(1031), in response to determining that the first thread (1030) of thefirst task (1040) requires the shared resource data (1080), SVDinformation (1081) associated with the shared resource data (1080).Requesting (1004) from the second thread (1031) the SVD information(1081) associated with the shared resource data (1080) may be carriedout by transmitting an active ‘GET’ message to the second thread. Inresponse to receiving the GET message, the second thread performs alookup in its SVD (1071) to retrieve the SVD information (1081). Thesecond thread may use the SVD information (1081) to retrieve the addressin the memory partition where the data is stored.

The method of FIG. 10 also includes the runtime optimizer (1000)receiving (1006) from the second thread (1031), the SVD information(1081). Receiving (1006) from the second thread (1031), the SVDinformation (1081) may be carried out by receiving an acknowledgementmessage in response to the GET message. The acknowledgement message mayinclude SVD information, such as the address in the shared memorypartition where the resource data is stored.

FIG. 11 sets forth a flow chart illustrating a further example method ofacquiring remote shared variable directory (SVD) information of aparallel computer according to embodiments of the present invention. Themethod of FIG. 11 is similar to the method of FIG. 10 in that the methodof FIG. 11 also includes: determining (1002) that a first thread (1030)of a first task (1040) requires shared resource data (1080) stored in amemory partition (1060) corresponding to a second thread (1031) of asecond task (1041); requesting (1004) from the second thread (1031), inresponse to determining that the first thread (1030) of the first task(1040) requires the shared resource data (1080), the SVD information(1081) associated with the shared resource data (1080); and receiving(1006) from the second thread (1031) the SVD information (1081).

The method of FIG. 11 also includes caching (1102) the SVD information(1081) in an SVD (1070) associated with the first thread (1030). Caching(1102) the SVD information (1081) in an SVD (1070) associated with thefirst thread (1030) may be carried out by identifying a handle withinthe SVD information; and storing the SVD information in an SVD at theentry corresponding to the key.

The method of FIG. 11 also includes using (1104) the cached SVDinformation (1153) in the SVD (1070) to access the shared resource data(1080) in the shared memory partition (1060) corresponding to the secondthread (1031). Using (1104) the cached SVD information (1153) in the SVD(1070) to access the shared resource data (1080) in the shared memorypartition (1060) corresponding to the second thread (1031) may becarried out by retrieving the address from the SVD; and using theaddress to access the shared memory partition of the second thread.

FIG. 12 sets forth a flow chart illustrating a further example method ofacquiring remote shared variable directory (SVD) information of aparallel computer according to embodiments of the present invention. Themethod of FIG. 12 is similar to the method of FIG. 10 in that the methodof FIG. 12 also includes: determining (1002) that a first thread (1030)of a first task (1040) requires shared resource data (1080) stored in amemory partition (1060) corresponding to a second thread (1031) of asecond task (1041); requesting (1004) from the second thread (1031), inresponse to determining that the first thread (1030) of the first task(1040) requires the shared resource data (1080), SVD information (1081)associated with the shared resource data (1080); and receiving (1006)from the second thread (1031) the SVD information (1081).

In the method of FIG. 12, however, determining (1002) that a firstthread (1030) of a first task (1040) requires shared resource data(1080) stored in a shared memory partition (1060) includes tracking(1202) which threads of a particular job are using a key (1090).Tracking (1202) which threads of a particular job are using a key (1090)may be carried out by monitoring initiation and use of a key by threadsof a particular job; and storing the initiation and use in a identifiertable.

In the method of FIG. 12, however, requesting (1004) from the secondthread (1031) the SVD information (1081) associated with the sharedresource data (1080) includes transmitting (1204) an active message(1252) using a parallel active message interface (PAMI) (1254).Transmitting (1204) an active message (1252) using a parallel activemessage interface (PAMI) (1254) may be carried out by transmitting anactive GET message.

In the method of FIG. 12, however, receiving (1006) from the secondthread (1031) the SVD information (1081) includes determining (1206)that the SVD information has not been updated by the second thread.Determining (1206) that the SVD information has not been updated by thesecond thread may be carried out by monitoring usage of keys in anapplication program.

Example embodiments of the present invention are described largely inthe context of a fully functional parallel computer that implementsacquiring remote shared variable directory (SVD) information. Readers ofskill in the art will recognize, however, that the present inventionalso may be embodied in a computer program product disposed uponcomputer readable storage media for use with any suitable dataprocessing system. Such computer readable storage media may be anystorage medium for machine-readable information, including magneticmedia, optical media, or other suitable media. Examples of such mediainclude magnetic disks in hard drives or diskettes, compact disks foroptical drives, magnetic tape, and others as will occur to those ofskill in the art. Persons skilled in the art will immediately recognizethat any computer system having suitable programming means will becapable of executing the steps of the method of the invention asembodied in a computer program product. Persons skilled in the art willrecognize also that, although some of the example embodiments describedin this specification are oriented to software installed and executingon computer hardware, nevertheless, alternative embodiments implementedas firmware or as hardware are well within the scope of the presentinvention.

As will be appreciated by those of skill in the art, aspects of thepresent invention may be embodied as method, apparatus or system, orcomputer program product. Accordingly, aspects of the present inventionmay take the form of an entirely hardware embodiment or an embodimentcombining software and hardware aspects (firmware, resident software,micro-code, microcontroller-embedded code, and the like) that may allgenerally be referred to herein as a “circuit,” “module,” “system,” or“apparatus.” Furthermore, aspects of the present invention may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

Any combination of one or more computer readable media may be utilized.Such a computer readable medium may be a computer readable signal mediumor a computer readable storage medium. A computer readable storagemedium may be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described in this specificationwith reference to flowchart illustrations and/or block diagrams ofmethods, apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof computer apparatus, methods, and computer program products accordingto various embodiments of the present invention. In this regard, eachblock in a flowchart or block diagram may represent a module, segment,or portion of code, which comprises one or more executable instructionsfor implementing the specified logical function(s). It should also benoted that, in some alternative implementations, the functions noted inthe block may occur out of the order noted in the figures. For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A method of acquiring remote shared variabledirectory (SVD) information in a parallel computer, the parallelcomputer comprising a plurality of threads of execution, the threadsorganized into tasks, the parallel computer further comprising memorypartitioned to provide each thread with a private memory and a sharedmemory, the method comprising: determining, by a runtime optimizer ofthe parallel computer, that a first thread of a first task requiresshared resource data stored in a memory partition corresponding to asecond thread of a second task; in response to determining that thefirst thread of the first task requires the shared resource data storedin the memory partition corresponding to the second thread, requestingfrom the second thread, by the runtime optimizer, SVD informationassociated with the shared resource data; and receiving from the secondthread, by the runtime optimizer, the SVD information associated withthe shared resource data.
 2. The method of claim 1 further comprising:caching, by the runtime optimizer, the SVD information in an SVDassociated with the first thread; and using the cached SVD informationin the SVD to access the shared resource data in the memory partitioncorresponding to the second thread.
 3. The method of claim 1 wherein theSVD information includes a key identifying the shared resource data, andan address indicating where the shared resource data is stored in thememory partition corresponding to the second thread.
 4. The method ofclaim 3 wherein determining, by a runtime optimizer of the parallelcomputer, that a first thread of a first task requires shared resourcedata stored in a memory partition corresponding to a second thread of asecond task includes tracking which threads of a particular job areusing the key.
 5. The method of claim 1 wherein requesting from thesecond thread, by the runtime optimizer, SVD information associated withthe shared resource data includes transmitting an active message using aparallel active message interface (PAMI).
 6. The method of claim 1wherein using the cached SVD information in the SVD to access theresource data in the shared memory partition corresponding to the secondthread includes determining that the SVD has not been updated by thesecond thread. 7-20. (canceled)